Strain variation in ppGpp concentration and RpoS levels in laboratory strains of Escherichia coli K-12

Laboratory strains and natural isolates of Escherichia coli differ in their level of stress resistance due to strain variation in the level of the sigma factor sigma(S) (or RpoS), the transcriptional master controller of the general stress response. We found that the high level of RpoS in one laboratory strain (MC4100) was partially dependent on an elevated basal level of ppGpp, an alarmone responding to stress and starvation. The elevated ppGpp was caused by two mutations in spoT, a gene associated with ppGpp synthesis and degradation. The nature of the spoT allele influenced the level of ppGpp in both MC4100 and another commonly used K-12 strain, MG1655. Introduction of the spoT mutation into MG1655 also resulted in an increased level of RpoS, but the amount of RpoS was lower in MG1655 than in MC4100 with either the wild-type or mutant spoT allele. In both MC4100 and MG1655, high ppGpp concentration increased RpoS levels, which in turn reduced growth with poor carbon sources like acetate. The growth inhibition resulting from elevated ppGpp was relieved by rpoS mutations. The extent of the growth inhibition by ppGpp, as well as the magnitude of the relief by rpoS mutations, differed between MG1655 and MC4100. These results together suggest that spoT mutations represent one of several polymorphisms influencing the strain variation of RpoS levels. Stress resistance was higher in strains with the spoT mutation, which is consistent with the conclusion that microevolution affecting either or both ppGpp and RpoS can reset the balance between self-protection and nutritional capability, the SPANC balance, in individual strains of E coli.

SRTM resample with short distance-low nugget kriging

The shuttle radar topography mission (SRTM), was flow on the space shuttle Endeavour in February 2000, with the objective of acquiring a digital elevation model of all land between 60 degrees north latitude and 56 degrees south latitude, using interferometric synthetic aperture radar (InSAR) techniques. The SRTM data are distributed at horizontal resolution of 1 arc-second (similar to 30m) for areas within the USA and at 3 arc-second (similar to 90m) resolution for the rest of the world. A resolution of 90m can be considered suitable for the small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it will not increase the level of detail of the original digital elevation model (DEM), but it will lead to a surface where there is the coherence of angular properties (i.e. slope, aspect) between neighbouring pixels, which is an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3"" to 1"". We present for a test area in western USA, which includes different adjustment schemes (changes in nugget effect value and in the interpolation radius) and comparisons with the original 1"" model of the area, with the national elevation dataset (NED) DEMs, and with other interpolation methods (splines and inverse distance weighted (IDW)). The basic concepts for using kriging to resample terrain data are: (i) working only with the immediate neighbourhood of the predicted point, due to the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; (ii) adding a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and; (iii) using a small value of nugget effect, to avoid smoothing that can obliterate terrain features. Drainages derived from the surfaces interpolated by kriging and by splines have a good agreement with streams derived from the 1"" NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1"" surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging.